O trabalho publicado em 2001 representa um estudo entre o cruzamento interespecífico entre M. guttatus e M. nasutus sobre especiação e adaptação floral. A construção de um mapa de ligação a partir de uma população F2 mostrou que próximo da metade dos marcadores apresentou significante distorção da taxa de transmissão, sugerindo uma divergência substancial entre as duas espécies. O mapa foi construído a partir de 255 marcadores em 14 grupos de ligação, com um comprimento total de 1780 cM Kosambi. Dentre esses 255 marcadores, haviam marcadores AFLP, microssatélites e marcadores baseados em gene.
Mapa resultante deste trabalho:
Novos locos foram genotipados agora e um novo mapa será construído a seguir:
Chamando a biblioteca ‘onemap’, lendo o arquivo ‘.raw’ contendo os novos marcadores e verificando o tipo de população em estudo através do ‘onemap’:
library('onemap')
library('knitr')
map_maker_data <- read_mapmaker(file = "m_feb06.raw")
## --Read the following data:
## Type of cross: f2
## Number of individuals: 287
## Number of markers: 418
## Missing trait values:
## fl: 11
## fs: 19
## ft: 11
## ll: 20
## nv: 12
## pa: 35
## pal: 11
## pv: 12
## sa: 11
## sl: 11
## ss: 28
## tl: 11
## tp: 12
## tw: 11
## vi: 12
## ww: 11
class(map_maker_data)
## [1] "onemap" "f2"
A população F2 contém 287 indivíduos e 418 marcadores agora.
Antes de construir o mapa é aconselhado avaliar visualmente a população, distribuição e o tipo dos marcadores utilizando as seguintes funções:
plot(map_maker_data)
plot_by_segreg_type(map_maker_data)
Os gráficos nos mostram que o conjunto de dados possui 213 marcadores codominantes (A.H.B), 92 marcadores dominantes para o alelo B (C.A) e 113 marcadores dominantes para o alelo A (D.B).
Há grande possibilidade que haja marcadores contendo as mesmas informações genotípicas. Estes marcadores que não agregam novas informações serão agrupados em compartimentos, ou ‘bins’, por meio da função ‘find_bins()’. Pois deixá-los na análise só irá aumentar o esforço computacional durante a montagem do mapa.
bins <- find_bins(map_maker_data); bins
## This is an object of class 'onemap_bin'
## No. individuals: 287
## No. markers in original dataset: 418
## No. of bins found: 418
## Average of markers per bin: 1
## Type of search performed: exact
bins_example <- create_data_bins(map_maker_data, bins)
Neste conjunto de dados não há marcas redundantes.
Essa etapa é necessária para verificar se os marcadores estão segregando de forma mendeliana, para isso o teste chi-quadrado é realizado:
f2_test <- test_segregation(bins_example); class(f2_test)
## [1] "onemap_segreg_test"
print(f2_test)[1:10, 1:5]
## Marker H0 Chi-square p-value % genot.
## 1 AA461 3:1 9.6012121 0.0019444897 95.82
## 2 AA420 3:1 0.8836364 0.3472076393 95.82
## 3 AA404 3:1 1.8436364 0.1745253406 95.82
## 4 AA384 3:1 3.9381818 0.0472017677 95.82
## 5 AA378 3:1 12.3648485 0.0004374931 95.82
## 6 AA371C 1:2:1 5.3927273 0.0674503413 95.82
## 7 AA361 3:1 4.8109091 0.0282801226 95.82
## 8 AA341 3:1 2.2412121 0.1343756065 95.82
## 9 AA311 3:1 5.4412121 0.0196670167 95.82
## 10 AA280 3:1 3.4048485 0.0650050838 95.82
Onde na coluna 1 são os marcadores. Na coluna 2 são as hipóteses nulas de que cada marcador está segregando de acordo com o tipo do marcador, se dominante ou codominante. Na coluna 3 é o teste de Chi-quadrado. Na coluna 4 o seu p-valor. E na coluna 5 a proporção dos indivíduos genotipados para esse marcador.
No entanto para declarar significância, devemos levar em conta a análise de múltiplos testes e fazer uma correção de Bonferroni:
Bonferroni_alpha(f2_test)
## [1] 0.0001196172
plot(f2_test)
O gráfico mostra os p-valores transformados usando o Log negativo na base 10 para melhor visualização. Para assegurar confiabilidade nos resultados, optamos por descartar as marcas que estão entre os 15% significantes, mesmo sendo uma correção conservativa.
Selecionamos apenas as marcas que estão entre os 85% não significantes:
select_segreg(f2_test, distorted = FALSE)
no_dist <- select_segreg(f2_test, distorted = FALSE, numbers = TRUE); no_dist
E agora o número de marcas restantes são:
length(no_dist)
## [1] 356
Agora iremos testar primeiro a fração de recombinação por pares e calcular a pontuação LOD:
twopts_f2 <- rf_2pts(input.obj = bins_example)
## Computing 87153 recombination fractions:
##
## 0% ........................................... 100%
LOD_sug <- suggest_lod(bins_example) # LOD_sug
O LOD mínimo sugerido é:
LOD_sug
## [1] 5.429707
Como não há informação dos cromossomos e posição dos marcadores, iremos levar em consideração apenas as informações de recombinação para montagem do mapa. Agora atribuiremos os marcadores aos devidos grupos de ligação:
mark_all_f2 <- make_seq(twopts_f2, no_dist)
set_map_fun(type = "kosambi")
Usaremos a função ‘group()’ para separar os marcadores por grupo, utilizando máxima fração de recombinação de 50%. Antes de utilizar o LOD sugerido, iremos analisar os LODs:
data <- data.frame(); data2 <- data.frame()
for (j in 1:10) {
LGs_f2 <- group(mark_all_f2, LOD = j, max.rf = 0.5)
for (i in 1:LGs_f2$n.groups){
LG1 <- make_seq(LGs_f2, i)
data <- data.frame(lod = j, grupo = i, num_markers = length(LG1$seq.num))
data2 <- rbind(data2, data);
}
}
## Selecting markers:
## group 1
## ............................................................
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## Selecting markers:
## group 1
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## Selecting markers:
## group 1
## ............................................................
## ............................................................
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## group 2
## .....
## Selecting markers:
## group 1
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## group 2
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## group 3
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## group 4
## ......................................
## group 5
## ........................................
## group 6
## .....
## Selecting markers:
## group 1
## ......
## group 2
## .....................
## group 3
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## ......................
## group 4
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## group 5
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## group 9
## ............
## group 10
## .....
## group 11
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## group 12
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## group 13
## .....
## group 14
## ..
## Selecting markers:
## group 1
## ......
## group 2
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## group 3
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## group 4
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## group 5
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## group 6
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## group 7
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## group 8
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## group 9
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## group 10
## .....
## group 11
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## group 12
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## group 13
## .....
## group 14
## ..
## Selecting markers:
## group 1
## ......
## group 2
## .....................
## group 3
## ........................
## group 4
## ......................................
## group 5
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## group 6
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## group 11
## .....
## group 12
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## group 13
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## group 14
## .....
## group 15
## ..
## Selecting markers:
## group 1
## ......
## group 2
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## group 3
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## group 4
## ......................................
## group 5
## ..............
## group 6
## ..................................................
## group 7
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## group 8
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## group 9
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## group 10
## ............
## group 11
## .....
## group 12
## ....................
## group 13
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## group 14
## .....
## group 15
## ..
## Selecting markers:
## group 1
## ....
## group 2
## .....
## group 3
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## group 4
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## group 5
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## group 6
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## group 7
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## group 8
## .
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## group 12
## .....
## group 13
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## group 15
## ......................
## group 16
## .....
## group 17
## ..
## Selecting markers:
## group 1
## ....
## group 2
## .....
## group 3
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## group 4
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## group 5
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## .
## group 9
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## group 10
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## group 11
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## .....
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## group 15
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## group 16
## .....
## group 17
## ..
table(data2$lod)
##
## 1 2 3 4 5 6 7 8 9 10
## 1 1 2 6 14 14 15 15 17 17
subset(data2, lod == "5")
## lod grupo num_markers
## 11 5 1 7
## 12 5 2 22
## 13 5 3 83
## 14 5 4 39
## 15 5 5 15
## 16 5 6 51
## 17 5 7 32
## 18 5 8 26
## 19 5 9 13
## 20 5 10 6
## 21 5 11 24
## 22 5 12 21
## 23 5 13 6
## 24 5 14 3
sum(subset(data2, lod =="5")[ , 3])
## [1] 348
(LGs_f2 <- group(mark_all_f2, LOD = 5, max.rf = 0.5))
## Selecting markers:
## group 1
## ......
## group 2
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## group 3
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## group 4
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## group 5
## ..............
## group 6
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## group 7
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## group 8
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## group 9
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## group 10
## .....
## group 11
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## group 12
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## group 13
## .....
## group 14
## ..
## This is an object of class 'group'
## It was generated from the object "mark_all_f2"
##
## Criteria used to assign markers to groups:
## LOD = 5 , Maximum recombination fraction = 0.5
##
## No. markers: 356
## No. groups: 14
## No. linked markers: 348
## No. unlinked markers: 8
##
## Printing groups:
## Group 1 : 7 markers
## AA461 AA341 BA416 BA374 CA174 CC447 AAT333
##
## Group 2 : 22 markers
## AA420 BA301 BA172 BA125 CA279 CA228 CB280 BD175 BD99 BC167 BC126 CC132 CC130 CC385C MgSTS316 MgSTS49 MgSTS106 MgSTS56 MgSTS457 MgSTS513 MgSTS589 MgSTS565
##
## Group 3 : 83 markers
## AA404 AA361 AA280 AA100 BA497 BA384 BA220 BA75 BB281 BB259 BB190 BB122 BB119 BB102 CA289 CA238 CA220 CA198 CA131 CA96 CB309 CB246 CB187 CB173 CB166 CB162 CB156 BD433 BD429 BD292 BD286 BD263 BD209 BD170 BD68 AA153C CA258C BB103C AAT261 AAT278 AAT374 AAT372 AG19 BC498 BC379 BC334 BC131 BC70 BC586C BC128C CC392 CC387 CC378 CC330 CC286 CC283 AAT312 AAT283 MgSTS27 MgSTS308 MgSTS43 MgSTS48 MgSTS474 MgSTS95 MgSTS50 MgSTS388 MgSTS332 MgSTS37 MgSTS34 MgSTS509 MgSTS251 MgSTS351 MgSTS293 MgSTS574B MgSTS574a MgSTS579 MgSTS535 MgSTS539 MgSTS609 MgSTS383 MgSTS441 MgSTS511 MgSTS214
##
## Group 4 : 39 markers
## AA384 AA166C AA66 BA210 BA113 BB208 CA497 CA415 CA399 CA384 CA297 CA233 CB329 CB257 BD411 BD243 BD239 BD130 BD115 AA346C AA374C BA279C CA183C BC512 BC321 BC216 BC192 CC531 CC270 MgSTS132 MgSTS228 MgSTS234 MgSTS455 MgSTS262 MgSTS362 MgSTS492 MgSTS347 MgSTS477 MgSTS542B
##
## Group 5 : 15 markers
## AA378 AA268 AA163 BA69 BB210 BB186 CB230 BD316C AA454C BC506 CC124 MgSTS40 MgSTS282A MgSTS255 MgSTS586
##
## Group 6 : 51 markers
## AA371C AA311 AA277 AA270 AA158 BA222 BA117 CA283 CA152C CB333 BD179 BD169 BD55 AAT230 AAT300 BC392 BC243 BC125 CC457 CC381 CC262 CC126 CC171C MgSTS21 MgSTS25 MgSTS22 MgSTS28 MgSTS105 MgSTS120 MgSTS58 MgSTS229 MgSTS323 MgSTS508 MgSTS529 MgSTS480 MgSTS426 MgSTS453 MgSTS456 MgSTS430 MgSTS314 MgSTS320 MgSTS606 MgSTS504A MgSTS504B MgSTS545 MgSTS542A MgSTS459 MgSTS220 MgSTS467 MgSTS431 MgSTS440
##
## Group 7 : 32 markers
## AA246 BA372 BA314 BA158 BB167 CA305 CA210 CA122 CB272 BD189C AAT217 AAT39 AAT211 AAT296 CYCB AAT242 CC450 CC359 CC338C MgSTS59 MgSTS69 MgSTS31 MgSTS76 MgSTS330 MgSTS381 MgSTS571 MgSTS590 MgSTS537 MgSTS538 MgSTS563 MgSTS504C MgSTS621
##
## Group 8 : 26 markers
## AA167 AA95 BA445 BA400 BA311 BB218 BB176 CA261 CA217 CA196 CA167 CB115 BA396C AAT222 BC108 BC83 CC540 CC402 LFY MgSTS468 MgSTS638 MgSTS536 MgSTS558 MgSTS611 MgSTS600 MgSTS470
##
## Group 9 : 13 markers
## AA137 BB216 CA75 BD371 BD251 CC93 MgSTS17 MgSTS18 MgSTS133 MgSTS350 MgSTS500 MgSTS578 MgSTS632
##
## Group 10 : 6 markers
## BA449 CC61 MgSTS26 MgSTS93 MgSTS344 MgSTS598
##
## Group 11 : 24 markers
## BA394 BA175 BB182 CA378 CA140 CB216 BD143 AA296C BA245C AAT308 AAT364 BC546 BC478 BC376 BC330 BC266 BC219 CC149 CC114 MgSTS36 MgSTS437 MgSTS113 MgSTS282B MgSTS438
##
## Group 12 : 21 markers
## BA334 BA145 BB198 CA315 CB172 CB126 CB55 CB263C AAT240 BC199 CC150 MgSTS55 MgSTS45 MgSTS11 MgSTS68 MgSTS326 MgSTS419 MgSTS577 MgSTS104 MgSTS622 MgSTS599
##
## Group 13 : 6 markers
## BA153 BD270 BC194C CC138 MgSTS98 MgSTS212
##
## Group 14 : 3 markers
## CA150 BC80 AP3
##
## Unlinked markers: 8 markers
## AA208 BB279 CA392 BC542 BC374 BC135 CC371 MgSTS23
Separando cada marcador em uma sequência referente ao seu grupo de ligação:
F2_LGs_list <- list()
for (i in 1:LGs_f2$n.groups) {
name <- paste0("LG" ,i , "_f2")
F2_LGs_list[[i]] <- assign(name, make_seq(LGs_f2, i))
}
Ordenando dentro do grupo 3 por ser o maior, o algoritmo utilizado será ‘Recombination Counting and Ordering’, a escolha se deu por ser o algoritmo que resultou no melhor mapa para esse grupo e tambḿe por ser o menor. Para motivos de comparação também foram utilizados o algoritmo ‘Rapid Chain Delineation (Doerge, 1996)’, o algoritmo ‘Unidirectional Growth (Tan and Fu, 2006)’ e o método que ordena os marcadores por uma abordagem de escalonamento multidimensional ‘MDS’:
LG3_rec_f2 <- record(LG3_f2); LG3_rec_f2 #Recombination Counting and Ordering
LG3_rcd_f2 <- rcd(LG3_f2); LG3_rcd_f2 #Rapid Chain Delineation
LG3_ug_f2 <- ug(LG3_f2); LG3_ug_f2 #Unidirectional Growth
LG3_mds_f2 <- mds_onemap(input.seq = LG3_f2, hmm = TRUE, mds.seq = TRUE, mapfn = "kosambi"); LG3_mds_f2 #MDS
Aqui estão os gráficos de ligação referentes a cada um dos algoritmos:
rf_graph_table(LG3_rcd_f2) #Rapid Chain Delineation
rf_graph_table(LG3_mds_f2)#MDS
rf_graph_table(LG3_ug_f2) #Unidirectional Growth
rf_graph_table(LG3_rec_f2) #Recombination Counting and Ordering
Aplicando o método ‘record’ para o ordenamento de todos os grupos de ligação:
F2_rec_list <- lapply(F2_LGs_list, record)
Agora ordenando todos os grupos utilizando a função ‘order_seq()’:
LGs_f2_ord_list <- lapply(F2_rec_list, order_seq, n.init = 5, subset.search = "twopt", twopt.alg = "rec", THRES = 3, touchdown = TRUE)
Testando o ordenamento ‘Safe’ para fazer o mapa:
LGs_f2_final <- lapply(LGs_f2_ord_list,make_seq, "safe")
Fazendo os gráficos de todos os grupos agora ordenados:
lapply(LGs_f2_final, rf_graph_table)
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Para checar ordens alternativas nós utilizaremos a função ‘ripple_seq’:
lapply(LGs_f2_final, ripple_seq, ws = 2, LOD = 3)
Utilizando a função ‘draw_map’ para desenhar o mapa e a função ‘draw_map2’ para gerar um arquivo em formato ‘.png’ do mapa:
draw_map(LGs_f2_final, names = TRUE, grid = TRUE, cex.mrk = 0.7)
draw_map2(LGs_f2_final, col.group = "#58A4B0", col.mark = "#335C81", output = "map_LGSafe.png")
Testando o ordenamento ‘Force’ para fazer o mapa:
LGs_f2_final <- lapply(LGs_f2_ord_list,make_seq, "force")
Fazendo os gráficos de todos os grupos agora ordenados:
lapply(LGs_f2_final, rf_graph_table)
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Para checar ordens alternativas nós utilizaremos a função ‘ripple_seq’:
lapply(LGs_f2_final, ripple_seq, ws = 2, LOD = 3)
Utilizaremos a função ‘draw_map’ para desenhar o mapa e a função ‘draw_map2’ para gerar um arquivo em formato ‘.png’ do mapa:
draw_map(LGs_f2_final, names = TRUE, grid = TRUE, cex.mrk = 0.7)
draw_map2(LGs_f2_final, col.group = "#58A4B0", col.mark = "#335C81", output = "map_LGForce.png")
knitr::include_graphics("map_LGForce.png")
knitr::include_graphics("map_LGSafe.png")
n <- 3
draw_map(LGs_f2_final[[n]], names = TRUE, grid = TRUE, cex.mrk = 0.7)
rf_graph_table(LGs_f2_final[[n]])
(F2_temp <- drop_marker(LGs_f2_final[[n]], 149)); (F2_temp <- map(F2_temp))
(F2_temp <- drop_marker(LGs_f2_final[[n]], c(149, 221))); (F2_temp <- map(F2_temp))
(F2_temp <- drop_marker(LGs_f2_final[[n]], c(149, 221, 119, 130, 7, 3, 125, 235))); (F2_temp <- map(F2_temp))
(F2_temp <- drop_marker(LGs_f2_final[[n]], c(149, 221, 217, 229, 372, 115, 64))); (F2_temp <- map(F2_temp))
#(F2_temp <- try_seq(input.seq = F2_temp, mrk = 149)) #A inserção do marcador '149' inflacionou o tamanho do mapa, por isso optamos por mantê-lo fora.
#(F2_temp <- make_seq(F2_temp, 52))
LGs_f2_final[[3]] <- F2_temp
rf_graph_table(LGs_f2_final[[3]])
O mesmo com o grupo 4:
n <- 4
(F2_temp <- drop_marker(LGs_f2_final[[n]], c(146, 77, 126))); (F2_temp <- map(F2_temp))
##
## Number of markers: 36
## Markers in the sequence:
## AA384 CB257 MgSTS234 CC270 BC321 MgSTS455 BC216 BC192 CC531 BD239 BD130 AA346C
## BA113 BA279C CA297 BB208 MgSTS228 MgSTS492 MgSTS262 MgSTS362 MgSTS477 AA166C
## AA66 CA183C BD243 CA497 AA374C CA233 CA415 CA399 BA210 MgSTS132 MgSTS347 BC512
## CB329 MgSTS542B
##
## Parameters not estimated.
##
## Printing map:
##
## Markers Position
##
## 4 AA384 0.00
## 111 CB257 27.56
## 293 MgSTS234 35.15
## 239 CC270 44.86
## 202 BC321 60.89
## 310 MgSTS455 71.04
## 206 BC216 94.81
## 208 BC192 111.62
## 223 CC531 115.26
## 137 BD239 129.80
## 145 BD130 137.07
## 155 AA346C 142.36
## 52 BA113 146.12
## 159 BA279C 154.74
## 81 CA297 161.51
## 62 BB208 164.09
## 291 MgSTS228 166.51
## 347 MgSTS492 175.40
## 321 MgSTS262 185.33
## 336 MgSTS362 194.81
## 395 MgSTS477 200.39
## 18 AA166C 208.22
## 24 AA66 216.99
## 161 CA183C 222.28
## 135 BD243 231.38
## 73 CA497 238.09
## 156 AA374C 242.55
## 89 CA233 251.92
## 74 CA415 260.05
## 75 CA399 269.89
## 42 BA210 272.30
## 289 MgSTS132 278.74
## 358 MgSTS347 284.10
## 191 BC512 289.08
## 107 CB329 293.34
## 403 MgSTS542B 297.16
##
## 36 markers log-likelihood: -3970.238
LGs_f2_final[[n]] <- F2_temp
rf_graph_table(LGs_f2_final[[n]])
#LGs_f2_final[[4]]
E o mesmo com o grupo 6:
n <- 6
(F2_temp <- drop_marker(LGs_f2_final[[n]], c(150, 246, 213))); (F2_temp <- map(F2_temp))
##
## Number of markers: 48
## Markers in the sequence:
## CC457 MgSTS508 MgSTS529 AA311 MgSTS440 MgSTS25 MgSTS542A MgSTS545 MgSTS314
## MgSTS120 BD179 MgSTS459 MgSTS320 AAT230 BA117 AA277 AA270 AA158 MgSTS105
## MgSTS21 MgSTS58 MgSTS431 MgSTS229 MgSTS220 BD169 CC171C BA222 MgSTS22 CB333
## AAT300 CA152C CC381 CA283 CC262 MgSTS504A MgSTS426 MgSTS28 MgSTS453 MgSTS504B
## MgSTS606 MgSTS323 MgSTS467 MgSTS430 MgSTS456 BC392 AA371C BC243 MgSTS480
##
## Parameters not estimated.
##
## Printing map:
##
## Markers Position
##
## 224 CC457 0.00
## 306 MgSTS508 7.93
## 309 MgSTS529 9.13
## 9 AA311 17.03
## 418 MgSTS440 24.56
## 267 MgSTS25 34.84
## 402 MgSTS542A 41.57
## 401 MgSTS545 52.90
## 354 MgSTS314 54.93
## 274 MgSTS120 58.00
## 139 BD179 60.07
## 411 MgSTS459 63.66
## 359 MgSTS320 67.28
## 181 AAT230 77.60
## 51 BA117 90.55
## 11 AA277 97.76
## 12 AA270 102.56
## 20 AA158 110.83
## 273 MgSTS105 117.93
## 264 MgSTS21 119.98
## 280 MgSTS58 133.54
## 417 MgSTS431 135.64
## 292 MgSTS229 137.55
## 412 MgSTS220 145.56
## 143 BD169 152.47
## 254 CC171C 156.89
## 39 BA222 162.65
## 268 MgSTS22 165.94
## 106 CB333 168.74
## 186 AAT300 169.81
## 99 CA152C 174.04
## 230 CC381 179.18
## 83 CA283 184.65
## 240 CC262 185.76
## 398 MgSTS504A 191.31
## 342 MgSTS426 200.65
## 272 MgSTS28 203.05
## 343 MgSTS453 204.42
## 399 MgSTS504B 204.80
## 389 MgSTS606 210.68
## 297 MgSTS323 212.08
## 414 MgSTS467 213.63
## 351 MgSTS430 213.92
## 344 MgSTS456 218.99
## 195 BC392 223.94
## 6 AA371C 236.71
## 204 BC243 259.34
## 312 MgSTS480 294.85
##
## 48 markers log-likelihood: -4499.367
LGs_f2_final[[n]] <- F2_temp
rf_graph_table(LGs_f2_final[[n]])
F2_LGs_list[[1]] <- make_seq(twopts_f2, c(8, 33, 226, 1, 28, 97, 258))
F2_LGs_list[[2]] <- make_seq(twopts_f2, c(252, 50, 2, 245, 90, 296, 38, 209, 45, 109, 302, 315, 345, 140, 317, 369, 388, 348, 244, 148, 212, 84))
F2_LGs_list[[3]] <- make_seq(twopts_f2, c(119, 7, 165, 116, 112, 138, 162, 238, 365, 10, 40, 71, 142, 341, 356, 200, 168, 72, 235, 322, 319, 231, 131, 313, 413, 92, 184, 237, 88, 364, 120, 53, 130, 218, 3, 125, 102, 118, 32, 255, 179, 386, 295, 366, 360, 25, 228, 340, 301, 104, 197, 56, 171, 133, 82, 193, 175, 70, 326, 349, 397, 269, 376, 211, 58, 95, 153, 357, 22, 299, 405, 124, 329, 408, 108, 256))
F2_LGs_list[[4]] <- make_seq(twopts_f2, c(4, 239, 111, 293, 310, 202, 145, 155, 52, 159, 137, 291, 62, 81, 206, 223, 208, 347, 321, 336, 395, 18, 24, 161, 135, 73, 156, 89, 74, 75, 42, 289, 358, 191, 107, 403))
F2_LGs_list[[5]] <- make_seq(twopts_f2, c(65, 247, 282, 371, 54, 113, 61, 129, 5, 13, 157, 19, 192, 332, 333))
F2_LGs_list[[6]] <- make_seq(twopts_f2, c(312, 204, 6, 195, 344, 351, 414, 297, 389, 399, 343, 272, 342, 398, 240, 83, 230, 99, 39, 268, 106, 186, 254, 143, 412, 292, 417, 280, 264, 273, 12, 20, 51, 11, 139, 181, 359, 411, 274, 354, 401, 402, 267, 418, 9, 309, 306, 224))
F2_LGs_list[[7]] <- make_seq(twopts_f2, c(46, 68, 14, 324, 409, 233, 166, 253, 374, 327, 352, 318, 103, 36, 151, 110, 363, 400, 368, 180, 173, 172, 225, 80, 303, 187, 188, 378, 316, 373, 94, 34))
F2_LGs_list[[8]] <- make_seq(twopts_f2, c(227, 27, 215, 174, 375, 222, 17, 67, 122, 96, 29, 87, 23, 37, 384, 257, 367, 160, 214, 59, 387, 415, 381, 311, 98, 93))
F2_LGs_list[[9]] <- make_seq(twopts_f2, c(21, 260, 249, 127, 279, 334, 262, 385, 362, 350, 105, 134, 60))
F2_LGs_list[[10]] <- make_seq(twopts_f2, c(26, 250, 278, 270, 404, 338))
F2_LGs_list[[11]] <- make_seq(twopts_f2, c(66, 101, 114, 78, 298, 392, 154, 201, 331, 185, 183, 314, 189, 198, 44, 158, 300, 144, 194, 205, 242, 203, 248, 30))
F2_LGs_list[[12]] <- make_seq(twopts_f2, c(241, 207, 63, 383, 304, 330, 48, 410, 170, 121, 281, 164, 79, 123, 416, 407, 285, 284, 390, 35, 117))
F2_LGs_list[[13]] <- make_seq(twopts_f2, c(132, 47, 243, 220, 283, 290))
F2_LGs_list[[14]] <- make_seq(twopts_f2, c(216, 100, 259))
(LG1 <- map(F2_LGs_list[[1]]))
##
## Printing map:
##
## Markers Position
##
## 8 AA341 0.00
## 33 BA374 10.67
## 226 CC447 17.81
## 1 AA461 25.68
## 28 BA416 39.84
## 97 CA174 48.25
## 258 AAT333 57.87
##
## 7 markers log-likelihood: -761.4381
(LG2 <- map(F2_LGs_list[[2]]))
##
## Printing map:
##
## Markers Position
##
## 252 CC385C 0.00
## 50 BA125 13.96
## 2 AA420 21.28
## 245 CC130 31.80
## 90 CA228 33.43
## 296 MgSTS316 40.51
## 38 BA301 71.28
## 209 BC167 81.30
## 45 BA172 88.99
## 109 CB280 90.82
## 302 MgSTS49 96.17
## 315 MgSTS106 97.07
## 345 MgSTS457 101.69
## 140 BD175 106.02
## 317 MgSTS56 110.68
## 369 MgSTS589 111.42
## 388 MgSTS565 123.73
## 348 MgSTS513 125.18
## 244 CC132 131.39
## 148 BD99 137.37
## 212 BC126 137.37
## 84 CA279 166.21
##
## 22 markers log-likelihood: -2139.862
(LG3 <- map(F2_LGs_list[[3]]))
##
## Printing map:
##
## Markers Position
##
## 119 CB162 0.00
## 7 AA361 10.51
## 165 BB103C 16.70
## 116 CB173 18.17
## 112 CB246 25.11
## 138 BD209 32.13
## 162 CA258C 36.12
## 238 CC283 39.30
## 365 MgSTS574a 44.36
## 10 AA280 48.34
## 40 BA220 54.14
## 71 BB119 61.29
## 142 BD170 61.29
## 341 MgSTS34 70.90
## 356 MgSTS251 79.71
## 200 BC334 89.24
## 168 AAT261 99.69
## 72 BB102 117.03
## 235 CC330 135.99
## 322 MgSTS50 192.24
## 319 MgSTS95 197.60
## 231 CC378 204.26
## 131 BD286 222.80
## 313 MgSTS474 228.44
## 413 MgSTS214 233.28
## 92 CA220 239.62
## 184 AG19 257.78
## 237 CC286 273.80
## 88 CA238 291.59
## 364 MgSTS574B 298.62
## 120 CB156 304.36
## 53 BA75 317.75
## 130 BD292 328.71
## 218 BC586C 337.87
## 3 AA404 342.57
## 125 BD429 345.05
## 102 CA131 349.11
## 118 CB166 349.25
## 32 BA384 357.45
## 255 AAT312 358.86
## 179 AAT372 436.82
## 386 MgSTS609 445.33
## 295 MgSTS308 453.28
## 366 MgSTS579 455.97
## 360 MgSTS293 461.18
## 25 BA497 467.83
## 228 CC392 476.52
## 340 MgSTS37 495.91
## 301 MgSTS48 498.72
## 104 CA96 503.21
## 197 BC379 521.34
## 56 BB281 521.35
## 171 AAT278 534.02
## 133 BD263 538.06
## 82 CA289 541.37
## 193 BC498 545.14
## 175 AAT374 549.13
## 70 BB122 551.79
## 326 MgSTS388 553.85
## 349 MgSTS509 554.13
## 397 MgSTS383 555.44
## 269 MgSTS27 557.92
## 376 MgSTS539 564.46
## 211 BC131 567.41
## 58 BB259 568.69
## 95 CA198 571.79
## 153 AA153C 580.09
## 357 MgSTS351 587.99
## 22 AA100 591.38
## 299 MgSTS43 594.67
## 405 MgSTS441 615.98
## 124 BD433 640.42
## 329 MgSTS332 660.42
## 408 MgSTS511 666.78
## 108 CB309 674.47
## 256 AAT283 702.08
##
## 76 markers log-likelihood: -7610.798
(LG4 <- map(F2_LGs_list[[4]]))
##
## Printing map:
##
## Markers Position
##
## 4 AA384 0.00
## 239 CC270 34.04
## 111 CB257 44.08
## 293 MgSTS234 52.06
## 310 MgSTS455 74.60
## 202 BC321 83.17
## 145 BD130 89.46
## 155 AA346C 94.51
## 52 BA113 98.24
## 159 BA279C 106.98
## 137 BD239 115.52
## 291 MgSTS228 122.92
## 62 BB208 126.58
## 81 CA297 130.28
## 206 BC216 134.72
## 223 CC531 146.19
## 208 BC192 153.49
## 347 MgSTS492 165.11
## 321 MgSTS262 174.74
## 336 MgSTS362 184.09
## 395 MgSTS477 189.64
## 18 AA166C 197.50
## 24 AA66 206.27
## 161 CA183C 211.55
## 135 BD243 220.65
## 73 CA497 227.37
## 156 AA374C 231.83
## 89 CA233 241.20
## 74 CA415 249.33
## 75 CA399 259.17
## 42 BA210 261.58
## 289 MgSTS132 268.01
## 358 MgSTS347 273.38
## 191 BC512 278.36
## 107 CB329 282.62
## 403 MgSTS542B 286.44
##
## 36 markers log-likelihood: -3937.076
(LG5 <- map(F2_LGs_list[[5]]))
##
## Printing map:
##
## Markers Position
##
## 65 BB186 0.00
## 247 CC124 6.79
## 282 MgSTS40 16.83
## 371 MgSTS586 38.33
## 54 BA69 41.54
## 113 CB230 46.11
## 61 BB210 47.65
## 129 BD316C 49.37
## 5 AA378 58.33
## 13 AA268 81.76
## 157 AA454C 92.39
## 19 AA163 101.69
## 192 BC506 117.72
## 332 MgSTS282A 137.04
## 333 MgSTS255 141.12
##
## 15 markers log-likelihood: -1789.227
(LG6 <- map(F2_LGs_list[[6]]))
##
## Printing map:
##
## Markers Position
##
## 312 MgSTS480 0.00
## 204 BC243 35.51
## 6 AA371C 58.14
## 195 BC392 70.91
## 344 MgSTS456 75.86
## 351 MgSTS430 80.93
## 414 MgSTS467 81.22
## 297 MgSTS323 82.77
## 389 MgSTS606 84.17
## 399 MgSTS504B 90.05
## 343 MgSTS453 90.43
## 272 MgSTS28 91.80
## 342 MgSTS426 94.20
## 398 MgSTS504A 103.54
## 240 CC262 109.08
## 83 CA283 110.20
## 230 CC381 115.65
## 99 CA152C 120.79
## 39 BA222 125.70
## 268 MgSTS22 130.15
## 106 CB333 132.62
## 186 AAT300 133.69
## 254 CC171C 138.14
## 143 BD169 142.62
## 412 MgSTS220 149.67
## 292 MgSTS229 157.67
## 417 MgSTS431 159.59
## 280 MgSTS58 161.69
## 264 MgSTS21 175.24
## 273 MgSTS105 177.32
## 12 AA270 183.58
## 20 AA158 191.70
## 51 BA117 201.78
## 11 AA277 205.06
## 139 BD179 213.56
## 181 AAT230 218.87
## 359 MgSTS320 229.24
## 411 MgSTS459 232.80
## 274 MgSTS120 237.68
## 354 MgSTS314 240.90
## 401 MgSTS545 243.00
## 402 MgSTS542A 254.28
## 267 MgSTS25 261.00
## 418 MgSTS440 271.25
## 9 AA311 278.75
## 309 MgSTS529 286.67
## 306 MgSTS508 287.86
## 224 CC457 295.80
##
## 48 markers log-likelihood: -4487.073
(LG7 <- map(F2_LGs_list[[7]]))
##
## Printing map:
##
## Markers Position
##
## 46 BA158 0.00
## 68 BB167 18.12
## 14 AA246 20.48
## 324 MgSTS76 24.56
## 409 MgSTS621 39.53
## 233 CC359 43.69
## 166 AAT217 51.68
## 253 CC338C 64.15
## 374 MgSTS538 75.08
## 327 MgSTS330 79.91
## 352 MgSTS381 83.98
## 318 MgSTS31 91.18
## 103 CA122 94.98
## 36 BA314 101.04
## 151 BD189C 110.65
## 110 CB272 129.67
## 363 MgSTS571 135.57
## 400 MgSTS504C 137.98
## 368 MgSTS590 140.22
## 180 AAT296 161.00
## 173 AAT211 168.72
## 172 AAT39 172.10
## 225 CC450 182.55
## 80 CA305 188.23
## 303 MgSTS59 192.07
## 187 CYCB 199.25
## 188 AAT242 207.19
## 378 MgSTS563 224.15
## 316 MgSTS69 224.80
## 373 MgSTS537 240.52
## 94 CA210 245.05
## 34 BA372 251.66
##
## 32 markers log-likelihood: -3760.048
(LG8 <- map(F2_LGs_list[[8]]))
##
## Printing map:
##
## Markers Position
##
## 227 CC402 0.00
## 27 BA445 22.11
## 215 BC83 39.56
## 174 AAT222 54.69
## 375 MgSTS536 67.72
## 222 CC540 88.16
## 17 AA167 97.37
## 67 BB176 97.39
## 122 CB115 103.07
## 96 CA196 108.06
## 29 BA400 113.72
## 87 CA261 113.93
## 23 AA95 121.47
## 37 BA311 127.21
## 384 MgSTS611 131.61
## 257 LFY 136.87
## 367 MgSTS638 144.67
## 160 BA396C 160.63
## 214 BC108 170.61
## 59 BB218 174.26
## 387 MgSTS600 180.97
## 415 MgSTS470 182.23
## 381 MgSTS558 186.44
## 311 MgSTS468 187.98
## 98 CA167 193.22
## 93 CA217 193.66
##
## 26 markers log-likelihood: -2669.008
(LG9 <- map(F2_LGs_list[[9]]))
##
## Printing map:
##
## Markers Position
##
## 21 AA137 0.00
## 260 MgSTS17 4.72
## 249 CC93 17.42
## 127 BD371 18.50
## 279 MgSTS133 26.75
## 334 MgSTS350 34.37
## 262 MgSTS18 46.93
## 385 MgSTS632 52.32
## 362 MgSTS578 60.27
## 350 MgSTS500 64.23
## 105 CA75 66.77
## 134 BD251 77.56
## 60 BB216 85.29
##
## 13 markers log-likelihood: -1459.726
(LG10 <- map(F2_LGs_list[[10]]))
##
## Printing map:
##
## Markers Position
##
## 26 BA449 0.00
## 250 CC61 5.09
## 278 MgSTS93 12.20
## 270 MgSTS26 13.39
## 404 MgSTS598 17.78
## 338 MgSTS344 20.19
##
## 6 markers log-likelihood: -590.8984
(LG11 <- map(F2_LGs_list[[11]]))
##
## Printing map:
##
## Markers Position
##
## 66 BB182 0.00
## 101 CA140 3.41
## 114 CB216 13.61
## 78 CA378 16.32
## 298 MgSTS36 21.55
## 392 MgSTS438 22.16
## 154 AA296C 34.44
## 201 BC330 40.49
## 331 MgSTS282B 63.10
## 185 AAT364 78.83
## 183 AAT308 91.73
## 314 MgSTS113 96.54
## 189 BC546 111.11
## 198 BC376 118.52
## 44 BA175 120.54
## 158 BA245C 124.84
## 300 MgSTS437 131.37
## 144 BD143 142.25
## 194 BC478 159.38
## 205 BC219 188.64
## 242 CC149 223.29
## 203 BC266 260.07
## 248 CC114 268.05
## 30 BA394 282.02
##
## 24 markers log-likelihood: -2782.989
(LG12 <- map(F2_LGs_list[[12]]))
##
## Printing map:
##
## Markers Position
##
## 241 CC150 0.00
## 207 BC199 16.28
## 63 BB198 26.87
## 383 MgSTS419 35.68
## 304 MgSTS68 36.66
## 330 MgSTS326 45.67
## 48 BA145 51.14
## 410 MgSTS622 63.22
## 170 AAT240 71.99
## 121 CB126 77.79
## 281 MgSTS55 93.13
## 164 CB263C 108.16
## 79 CA315 114.31
## 123 CB55 137.78
## 416 MgSTS599 142.74
## 407 MgSTS104 153.85
## 285 MgSTS11 164.06
## 284 MgSTS45 165.29
## 390 MgSTS577 166.22
## 35 BA334 171.71
## 117 CB172 196.14
##
## 21 markers log-likelihood: -2432.026
(LG13 <- map(F2_LGs_list[[13]]))
##
## Printing map:
##
## Markers Position
##
## 132 BD270 0.00
## 47 BA153 18.58
## 243 CC138 31.63
## 220 BC194C 48.97
## 283 MgSTS98 69.52
## 290 MgSTS212 75.25
##
## 6 markers log-likelihood: -858.6445
(LG14 <- map(F2_LGs_list[[14]]))
##
## Printing map:
##
## Markers Position
##
## 216 BC80 0.00
## 100 CA150 10.74
## 259 AP3 14.70
##
## 3 markers log-likelihood: -396.303
draw_map(LG3, names = TRUE, grid = TRUE, cex.mrk = 0.7)
draw_map(list(LG1, LG2, LG3, LG4, LG5, LG6, LG7, LG8, LG9, LG10, LG11, LG12, LG13, LG14), names=T, cex.mrk = 0.6)
draw_map2(list(LG1, LG2, LG3, LG4, LG5, LG6, LG7, LG8, LG9, LG10, LG11, LG12, LG13, LG14) , col.group = "#58A4B0", col.mark = "#D52941", output = "map_LG_editado.png")
## Completed
## Output file: /home/melina/Desktop/Semestre 1/Biometria de marcadores genéticos/Aula 7/map_LG_editado(9).png
maps.list <- list(LG1, LG2, LG3, LG4, LG5, LG6, LG7, LG8, LG9, LG10, LG11, LG12, LG13, LG14)
include_graphics(c("map_LG_editado.png", "map_LGForce.png"))